As summarized in fig:problemsummary, our system provides a flexible
object detection algorithm that requires a very small training set of images of novel objects, where
each image is annotated only with image-level labels.
As such, it is suitable
for classifying and detecting objects given only images provided, for
instance, by an internet image search for images of novel classes.
In comparison to supervised few-shot object detection approaches, e.g.,  [1]}, [2]}, [3]}, [4]},
where manually labeled bounding box annotations are required, this is a
more realistic setting to learn an object detector on novel examples with applications
like robotics [5]} or video object segmentation [6]}.
